Although familial susceptibility to glioma is known, the genetic basis for this susceptibility remains unidentified in the majority of glioma-specific families. An alternative approach to identifying such genes is to examine cancer pedigrees, which include glioma as one of several cancer phenotypes, to determine whether common chromosomal modifications might account for the familial aggregation of glioma and other cancers.
Germline rearrangements in 146 glioma families (from the Gliogene Consortium; http://www.gliogene.org/) were examined using multiplex ligation-dependent probe amplification. These families all had at least 2 verified glioma cases and a third reported or verified glioma case in the same family or 2 glioma cases in the family with at least one family member affected with melanoma, colon, or breast cancer.The genomic areas covering TP53, CDKN2A, MLH1, and MSH2 were selected because these genes have been previously reported to be associated with cancer pedigrees known to include glioma.
We detected a single structural rearrangement, a deletion of exons 1-6 in MSH2, in the proband of one family with 3 cases with glioma and one relative with colon cancer.
Large deletions and duplications are rare events in familial glioma cases, even in families with a strong family history of cancers that may be involved in known cancer syndromes.
CDKN2A/B; family history; glioma; MLH1; MSH2; TP53
Glioma is a rare, but highly fatal, cancer that accounts for the majority of malignant primary brain tumors. Inherited predisposition to glioma has been consistently observed within non-syndromic families. Our previous studies, which involved non-parametric and parametric linkage analyses, both yielded significant linkage peaks on chromosome 17q. Here, we use data from next generation and Sanger sequencing to identify familial glioma candidate genes and variants on chromosome 17q for further investigation. We applied a filtering schema to narrow the original list of 4830 annotated variants down to 21 very rare (<0.1% frequency), non-synonymous variants. Our findings implicate the MYO19 and KIF18B genes and rare variants in SPAG9 and RUNDC1 as candidates worthy of further investigation. Burden testing and functional studies are planned.
Sophisticated statistical analyses of incidence frequencies are often required for various epidemiologic and biomedical applications. Among the most commonly applied methods is Pearson's χ2 test, which is structured to detect non-specific anomalous patterns of frequencies and is useful for testing the significance for incidence heterogeneity. However, the Pearson's χ2 test is not efficient for assessing the significance of frequency in a particular cell (or class) to be attributed to chance alone. We recently developed statistical tests for detecting temporal anomalies of disease cases based on maximum and minimum frequencies; these tests are actually designed to test of significance for a particular high or low frequency. We show that our proposed methods are more sensitive and powerful for testing extreme cell counts than is the Pearson's χ2 test. We elucidated and illustrated the differences in sensitivity among our tests and the Pearson's χ2 test by analyzing a data set of Langerhans cell histiocytosis cases and its hypothetical sets. We also computed and compared the statistical power of these methods using various sets of cell numbers and alternative frequencies. Our study will provide investigators with useful guidelines for selecting the appropriate tests for their studies.
classical occupancy model; extreme value; maximum; minimum; temporal anomalies
Symptom clusters, the multiple, co-occurring symptoms experienced by cancer patients, are debilitating and affects quality of life. We assessed if a panel of immune-response genes may underlie the co-occurrence of severe pain, depressed mood and fatigue and help identify patients with severe versus non-severe symptom clusters.
Symptoms were assessed at presentation, prior to cancer treatment in 599 newly diagnosed lung cancer patients. We applied cluster analyses to determine the patients with severe versus non-severe symptom clusters of pain, depressed mood, and fatigue.
Two homogenous clusters were identified. One hundred sixteen patients (19%) comprised the severe symptom cluster, reporting high intensity of pain, depressed mood and fatigue and 183 (30%) patients reported low intensity of these symptoms. Using Bayesian model averaging methodology, we found that of the 55 SNPs assessed, an additive effect of mutant alleles in ENOS (-1474 T/A) (Posterior Probability of Inclusion (PPI) = 0.78, OR = 0.54, 95% CI = (0.31, 0.93); IL1B T-31C (PPI = 0.72, OR = 0.55, 95% CI = (0.31, 0.97)); TNFR2 Met196Arg (PPI = 0.70; OR=1.85;95%CI=(1.03,3.36)); PTGS2 exon 10+837T>C (PPI = 0.69, OR = 0.54, 95%CI = (0.28, 0.99)); and IL10RB Lys47Glu (PPI = 0.68; OR=1.74; 95%CI=(1.04,2.92)) were predictive for symptom clusters.
Genetic polymorphisms may facilitate identification of high risk patients and development of individualized symptom therapies.
pain; depression; fatigue; cytokines; symptoms; genes; epidemiology; lung cancer; SNPs
Li-Fraumeni syndrome (LFS) is a rare familial cancer syndrome characterized by early cancer onset, diverse tumor types, and multiple primary tumors. Germ-line p53 mutations have been identified in most LFS families. A high-frequency genetic variant of single-nucleotide polymorphism (SNP) 309 in the MDM2 gene was recently confirmed to be a modifier of cancer risk in several case-control studies: substantially earlier cancer onset was observed in SNP309 G-allele carriers than in wild-type individuals by 7–16 years. However, risk analyses in family studies that jointly account for measured hereditary p53 mutations and SNP309 have not been evaluated. Here, we extended the statistical method that we recently developed to determine the combined effects of measured p53 mutations, SNP309, and gender and their interactions simultaneously. The method is structured for age-specific risk models based on Cox proportional hazards regression for censored age-of-onset traits. We analyzed the cancer incidence in 19 extended pedigrees with multiple germ-line p53 mutations ascertained through clinical LFS phenotype. The dataset consisted of 463 individuals with 129 p53 mutation carriers. Our analyses showed that the p53 germ-line mutation and its interaction with gender were strongly associated with familial cancer incidence and that the association between SNP309 and increased cancer risk was modest. In contrast with several outcomes in case-control studies, the interaction between SNP309 and p53 mutation was not statistically significant. The causal role of SNP309 in family studies was consistent with a previous finding that SNP309 G-alleles are associated with accelerated tumor formation in patients with sporadic and hereditary cancers.
A report by the National Cancer Institute identified that an important gap in symptom research is the investigation of multiple symptoms of cancer that might identify common biological mechanisms among cancer-related symptoms.
We applied novel statistical methods to assess whether variants of 37 inflammation genes may serve as biologic markers of risk for severe pain, depressed mood, and fatigue in non-Hispanic white patients with non-small cell lung cancer.
Pain, fatigue, and depressed mood were assessed prior to cancer treatment. We used a generalized, multivariate, classification tree approach to explore the influence of single nucleotide polymorphisms in the inflammation genes in pain, depressed mood, and fatigue in lung cancer patients.
Among patients with advanced-stage disease, IL-8-T251A was the most relevant genetic factor for pain (odds ratio [OR]=2.18, 95% confidence interval [CI]=1.34,3.55; P=0.001), depressed mood (OR=0.37, 95% CI=0.14,1.0), and fatigue (OR=2.07, 95% CI=1.16,3.70). Among those with early-stage NSCLC, variants in the IL-10 receptor were relevant for fatigue among women. Specifically, women with genotype Lys_Glu or Glu_Glu in the IL-10 gene had a 0.49 times lower risk of severe fatigue compared with those with genotype Lys_Lys (OR=0.49, 95% CI=0.25, 0.92; P=0.027). Among men with early-stage lung cancer, a marginal significance was observed for IL-1A C-889T, C/T or T/T genotype: these men had a lower risk of severe fatigue compared with those with genotype C/C (OR=0.38, 95% CI=0.13, 1.06).
The interaction of multiple inflammation genes, along with non-genetic factors, underlies the occurrence of symptoms. IL-8 and IL-10 may serve as potential targets for treating multiple symptoms of cancer.
Pain; depression; fatigue; cytokines; symptoms; genes
The underlying ethos of dbGaP is that access to these data by secondary data analysts facilitates advancement of science. NIH has required that genome-wide association study data be deposited in the Database of Genotypes and Phenotypes (dbGaP) since 2003. In 2013, a proposed updated policy extended this requirement to next-generation sequencing data. However, recent literature and anecdotal reports suggest lingering logistical and ethical concerns about subject identifiability, informed consent, publication embargo enforcement, and difficulty in accessing dbGaP data. We surveyed the International Genetic Epidemiology Society (IGES) membership about their experiences. One hundred and seventy five (175) individuals completed the survey, a response rate of 27%. Of respondents who received data from dbGaP (43%), only 32% perceived the application process as easy but most (75%) received data within five months. Remaining challenges include difficulty in identifying an institutional signing official and an overlong application process. Only 24% of respondents had contributed data to dbGaP. Of these, 31% reported local IRB restrictions on data release; an additional 15% had to reconsent study participants before depositing data. The majority of respondents (56%) disagreed that the publication embargo period was sufficient. In response, we recommend longer embargo periods and use of varied data-sharing models rather than a one-size-fits-all approach.
data sharing; identifiability; GWAS; ELSI; ethics; publication embargo; collaboration
To determine the difference in the risk factors for systemic hypertension in preterm and term infants in the neonatal intensive care unit (NICU).
Data were collected from an existing database of NICU children and confirmed by chart-review. Systemic hypertension was defined when 3 separate measurements of systolic and/or diastolic blood pressure were >95th% percentile and an anti-hypertensive medication was administered for > 2 weeks in the NICU.
From 4,203 infants, we identified 53 (1.3%) with treated hypertension; of whom 74% were preterm, 11% required surgical intervention and 85% required medications upon discharge. The pressure of a patent ductus arteriosus, umbilical catheterization, left ventricular hypertrophy, hypertensive medication at discharge and mortality was similar between the term and preterm. The major risk factors for preterm infants, especially those below 28 weeks gestation, were bronchopulmonary dysplasia and iatrogenic factors, but, in term infants, they were systemic diseases. Term infants were diagnosed with hypertension earlier during hospitalization, had a shorter duration of stay in NICU, and had higher incidence of hypertension needing more than 3 medications than preterm infants.
Perinatal risk factors are significant contributors to infantile hypertension. Term infants were diagnosed with hypertension earlier, had a shorter duration of stay, and had a higher incidence of resistant hypertension than preterm infants.
Hypertension; risk factors; race; blood pressure; prematurity; infants; neonates; etiology
The genetic heritability for sensation-seeking tendencies ranges from 40 to 60%. Sensation-seeking behaviors typically manifest during adolescence and are associated with alcohol and cigarette experimentation in adolescents. Social disinhibition is an aspect of sensation-seeking that is closely tied to cigarette and alcohol experimentation.
We examined the contribution of candidate genes to social disinhibition among 1132 Mexican origin youth in Houston, Texas, adjusting for established demographic and psychosocial risk factors. Saliva samples were obtained at baseline in 2005–06, and social disinhibition and other psychosocial data were obtained in 2008–09. Participants were genotyped for 672 functional and tagging SNPs potentially related to sensation-seeking, risk-taking, smoking, and alcohol use.
Six SNPs were significantly associated with social disinhibition scores, after controlling for false discovery and adjusting for population stratification and relevant demographic/psychosocial characteristics. Minor alleles for three of the SNPs (rs1998220 on OPRM1; rs9534511 on HTR2A; and rs4938056 on HTR3B) were associated with increased risk of social disinhibition, while minor alleles for the other three SNPs (rs1003921 on KCNC1; rs16116 downstream of NPY; and rs16870286 on LINC00518) exhibited a protective effect. Age, linguistic acculturation, thrill and adventure-seeking, and drug and alcohol-seeking were all significantly positively associated with increased risk of social disinhibition in a multivariable model (P < 0.001).
These results add to our knowledge of genetic risk factors for social disinhibition. Additional research is needed to verify whether these SNPs are associated with social disinhibition among youth of different ethnicities and nationalities, and to elucidate whether and how these SNPs functionally contribute to social disinhibition.
Genetic association study; Mexican-origin youth; sensation seeking; SNPs; social disinhibition
Several methods have been proposed to account for multiple comparisons in genetic association studies. However, investigators typically test each of the SNPs using multiple genetic models. Association testing using the Cochran-Armitage test for trend assuming an additive, dominant, or recessive genetic model, is commonly performed. Thus, each SNP is tested three times. Some investigators report the smallest p-value obtained from the three tests corresponding to the three genetic models, but such an approach inherently leads to inflated type 1 errors. Because of the small number of tests (three) and high correlation (functional dependence) among these tests, the procedures available for accounting for multiple tests are either too conservative or fail to meet the underlying assumptions (e.g., asymptotic multivariate normality or independence among the tests).
We propose a method to calculate the exact p-value for each SNP using different genetic models. We performed simulations, which demonstrated the control of type 1 error and power gains using the proposed approach. We applied the proposed method to compute p-value for a polymorphism eNOS -786T>C which was shown to be associated with breast cancer risk.
Our findings indicate that the proposed method should be used to maximize power and control type 1 errors when analyzing genetic data using additive, dominant, and recessive models.
Genetic association; Multiple testing; Cochran-Armitage trend test; Genetic models; Exact p-value
We are now well into the sequencing era of genetic analysis, and methods to investigate rare variants associated with disease remain in high demand. Currently, the more common rare variant analysis methods are burden tests and variance component tests. This report introduces a burden test known as the modified replication based sum statistic and evaluates its performance, and the performance of other common burden and variance component tests under the setting of a small sample size (103 total cases and controls) using the Genetic Analysis Workshop 18 simulated data with complete knowledge of the simulation model. Specifically we look at the variable threshold sum statistic, replication-based sum statistics, the C-alpha, and sequence kernel association test. Using minor allele frequency thresholds of less than 0.05, we find that the modified replication based sum statistic is competitive with all methods and that using 103 individuals leads to all methods being vastly underpowered. Much larger sample sizes are needed to confidently find truly associated genes.
Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.
Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.
We have previously identified tagSNPs at 8q24.21 influencing glioma risk. We have sought to fine-map the location of the functional basis of this association using data from four genome-wide association studies, comprising a total of 4147 glioma cases and 7435 controls. To improve marker density across the 700 kb region, we imputed genotypes using 1000 Genomes Project data and high-coverage sequencing data generated on 253 individuals. Analysis revealed an imputed low-frequency SNP rs55705857 (P = 2.24 × 10−38) which was sufficient to fully capture the 8q24.21 association. Analysis by glioma subtype showed the association with rs55705857 confined to non-glioblastoma multiforme (non-GBM) tumours (P = 1.07 × 10−67). Validation of the non-GBM association was shown in three additional datasets (625 non-GBM cases, 2412 controls; P = 1.41 × 10−28). In the pooled analysis, the odds ratio for low-grade glioma associated with rs55705857 was 4.3 (P = 2.31 × 10−94). rs55705857 maps to a highly evolutionarily conserved sequence within the long non-coding RNA CCDC26 raising the possibility of direct functionality. These data provide additional insights into the aetiological basis of glioma development.
Since smoking has a profound impact on socioeconomic disparities in illness and death, it is crucial that vulnerable populations of smokers be targeted with treatment. The US Public Health Service recommends that all patients be asked about their smoking at every visit, and that smokers be given brief advice to quit and referred to treatment.
Initiatives to facilitate these practices include the 5 A’s (i.e., Ask, Advise, Assess, Assist, Arrange) and Ask Advise Refer (AAR). Unfortunately, primary care referrals are low, and most smokers referred fail to enroll. This study evaluated the efficacy of the Ask Advise Connect (AAC) approach to linking smokers with treatment in a large, safety-net public healthcare system.
Pair-matched-two-treatment arm group-randomized trial.
Ten safety-net clinics in Houston, TX.
Clinics were randomized to AAC (n=5; intervention) or AAR (n=5; control). Licensed Vocational Nurses (LVNs) were trained to assess and record the smoking status of all patients at all visits in the electronic health record (EHR). Smokers were given brief advice to quit. In AAC, the names and phone numbers of smokers who agreed to be connected were sent electronically to the Texas Quitline daily, and patients were proactively called within 48 hours. In AAR, smokers were offered a Quitline referral card and encouraged to call on their own. Data were collected between June 2010 and March 2012 and analyzed in 2012.
Main Outcome Measure
The primary outcome – impact – was defined as the proportion of identified smokers that enrolled in treatment.
The impact (proportion of identified smokers who enrolled in treatment) of AAC (14.7%) was significantly greater than the impact of AAR (0.5%), t (4) = 14.61, p = 0.0001, OR = 32.10 (95% CI 16.60–62.06).
AAC has tremendous potential to reduce tobacco-related health disparities.
In many genetic disorders in which a primary disease-causing locus has been identified, evidence for additional trait variation due to genetic factors exists. These findings have led to studies seeking secondary “modifier” loci. Identification of modifier loci provides insight into disease mechanisms and may provide additional screening and treatment targets. We believe that modifier loci can be identified by re-analysis of genome screen data while controlling for primary locus effects. To test this hypothesis, we simulated multiple replicates of typical genome screening data on to two real family structures from a study of hypertrophic cardiomyopathy. With this marker data, we simulated two trait models with characteristics similar to one measure of hypertrophic cardiomyopathy. Both trait models included 3 genes. In the first, the trait was influenced by a primary gene, a secondary “modifier” gene, and a third very small effect gene. In the second, we modeled an interaction between the first two genes. We examined power and false positive rates to map the secondary locus while controlling for the effect of the primary locus with two types of analyses. First, we examine Monte Carlo Markov chain (MCMC) simultaneous segregation and linkage analysis as implemented in Loki, for which we calculated two scoring statistics. Second, we calculate LOD scores using an individual-specific liability class based on the quantitative trait value. We find that both methods produce scores that are significant on a genome-wide level in some replicates. We conclude that mapping of modifier loci in existing samples is possible with these methods.
Modifier gene; Complex trait; Statistical Genetics; Monte Carlo Markov chain; linkage analysis
While certain inherited syndromes (e.g. Neurofibromatosis or Li-Fraumeni) are associated with an increased risk of glioma, most familial gliomas are non-syndromic. This study describes the demographic and clinical characteristics of the largest series of non-syndromic glioma families ascertained from 14 centres in the United States (US), Europe and Israel as part of the Gliogene Consortium.
Families with 2 or more verified gliomas were recruited between January 2007 and February 2011. Distributions of demographic characteristics and clinical variables of gliomas in the families were described based on information derived from personal questionnaires.
The study population comprised 841 glioma patients identified in 376 families (9797 individuals). There were more cases of glioma among males, with a male to female ratio of 1.25. In most families (83%), 2 gliomas were reported, with 3 and 4 gliomas in 13% and 3% of the families, respectively. For families with 2 gliomas, 57% were among 1st-degree relatives, and 31.5% among 2nd-degree relatives. Overall, the mean (±standard deviation [SD]) diagnosis age was 49.4 (±18.7) years. In 48% of families with 2 gliomas, at least one was diagnosed at <40 y, and in 12% both were diagnosed under 40 y of age. Most of these families (76%) had at least one grade IV glioblastoma multiforme (GBM), and in 32% both cases were grade IV gliomas. The most common glioma subtype was GBM (55%), followed by anaplastic astrocytoma (10%) and oligodendroglioma (8%). Individuals with grades I–II were on average 17 y younger than those with grades III–IV.
Familial glioma cases are similar to sporadic cases in terms of gender distribution, age, morphology and grade. Most familial gliomas appear to comprise clusters of two cases suggesting low penetrance, and that the risk of developing additional gliomas is probably low. These results should be useful in the counselling and clinical management of individuals with a family history of glioma.
Glioma; Familial glioma; Clinical characteristics; Genetic counselling
Unlike genome-wide association studies, few comprehensive studies of copy number variation's contribution to complex human disease susceptibility have been performed. Copy number variations are abundant in humans and represent one of the least well-studied classes of genetic variants; in addition, known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering. Therefore, we performed a genome-wide study of association between deletion or excess homozygosity and rheumatoid arthritis using high-density 550 K SNP genotype data from a genome-wide association study. We used a genome-wide statistical method that we recently developed to test each contiguous SNP locus between 868 cases and 1194 controls to detect excess homozygosity or deletion variants that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting the known deletion variants on major histocompatibility complex, we identified 4.3 and 28 kb clusters on chromosomes 10p and 13q, respectively, which were significant at a Bonferroni-type-corrected 0.05 nominal significant level. Independently, we performed analyses using PennCNV, an algorithm for identifying and cataloging copy numbers for individuals based on a hidden Markov model, and identified cases and controls that had chromosomal segments with copy number <2. Using Fisher's exact test for comparing the numbers of cases and controls with copy number <2 per SNP, we identified 26 significant SNPs (protective; more controls than cases) aggregating on chromosome 14 with P-values <10−8.
Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype.
CHRNA5; CHRNA3; CHRNB4; meta-analysis; nicotine; smoke
Many studies examining genetic influences on physical activity (PA) have evaluated the impact of single nucleotide polymorphisms (SNPs) related to the development of lifestyle-related chronic diseases, under the hypothesis that they would be associated with PA. However, PA is a multi-determined behavior and associated with a multitude of health consequences. Thus, examining a broader range of candidate genes associated with a boarder range of PA correlates may provide new insights into the genetic underpinnings of PA. In this study we focus on one such correlate – sensation seeking behavior. Participants (N=1,130 Mexican origin youth) provided a saliva sample and data on PA and sensation seeking tendencies in 2008–09. Participants were genotyped for 630 functional and tagging variants in the dopamine, serotonin, and cannabinoid pathways. Overall 30% of participants (males – 37.6%; females – 22.0%) reported ≥60 minutes of PA on five out of seven days. After adjusting for gender, age and population stratification, and applying the Bayesian False Discovery Probability approach for assessing noteworthiness, four gene variants were significantly associated with PA. In a multivariable model, being male, having higher sensation seeking tendencies and at least one copy of the minor allele for SNPs in ACE (rs8066276 OR=1.44; p=0.012) and TPH2 (rs11615016 OR=1.73; p=0.021) were associated with increased likelihood of meeting PA recommendations. Participants with at least one copy of the minor allele for SNPs in SNAP25 (rs363035 OR=0.53; p=0.005) and CNR1 (rs6454672 OR=0.62; p=0.022) have decreased likelihood of meeting PA recommendations. Our findings extend current knowledge of the complex relationship between PA and possible genetic underpinnings.
Physical Activity; Genes; Sensation Seeking; Mexican origin youth
We studied whether a melanoma survivor-centered intervention was more effective than materials available to the general public in increasing children’s sun protection.
In a randomized controlled trial, melanoma survivors (n=340) who had a child ≤12 years received a targeted sun protection intervention (DVD and booklets) or standard education. Primary outcomes were children’s sunburns, children’s sun protection, and survivors’ psychosocial factors at baseline and postintervention (1 and 4 months).
The intervention increased children’s sunscreen reapplication at 1 month (P = 0.002) and use of wide-brimmed hats at 4 months (P = 0.045). There were no effects on other behaviors or sunburns. The intervention improved survivors’ hats/clothing self-efficacy at both follow-up assessments (P = 0.026, 0.009). At 4 months, the intervention improved survivors’ clothing intentions (P = 0.029), knowledge (P = 0.010), and outcome expectations for hats (P = 0.002) and clothing (P = 0.037). Children’s sun protection increased with survivors’ intervention use. The intervention was less effective in survivors who were female or who had a family history, older children, or children with higher baseline sun protection scores.
A melanoma survivor-centered sun protection intervention can improve some child and survivor outcomes. The intervention may be more effective in survivors who have younger children or less experience with sun protection. Intervention delivery must be enhanced to maximize use.
This is the first study to examine a sun protection intervention for children of melanoma survivors. Findings will guide interventions for this important population at increased melanoma risk.
Melanoma; Prevention & Control; Survivors; Child; Health Behavior
Despite extensive research on the topic, glioma etiology remains largely unknown. Exploration of potential interactions between single-nucleotide polymorphisms (SNPs) of immune genes is a promising new area of glioma research. The case-only study design is a powerful and efficient design for exploring possible multiplicative interactions between factors that are independent of one another. The purpose of our study was to use this exploratory design to identify potential pair wise SNP-SNP interactions from genes involved in several different immune-related pathways for investigation in future studies.
The study population consisted of two case groups: 1224 histological-confirmed, non-Hispanic white glioma cases from the U.S. and a validation population of 634 glioma cases from the U.K. Polytomous logistic regression, in which one SNP was coded as the outcome and the other SNP was included as the exposure, was utilized to calculate the odds ratios of the likelihood of cases simultaneously having the variant alleles of two different SNPs. Potential interactions were examined only between SNPs located in different genes or chromosomes.
Using this data-mining strategy, we found 396 significant SNP-SNP interactions among polymorphisms of immune-related genes that were present in both the U.S. and U.K. study populations.
This exploratory study was conducted for the purpose of hypothesis generation, and thus has provided several new hypotheses that can be tested using traditional case-control study designs to obtain estimates of risk.
This is the first study, to our knowledge, to take this novel approach to identifying SNP-SNP interactions relevant to glioma etiology.
Several national healthcare-based smoking cessation initiatives have been recommended to facilitate the delivery of evidence-based treatments such as those delivered by quitlines. The most notable examples are the 5 A’s (i.e., Ask, Advise, Assess, Assist, Arrange) and Ask Advise Refer (AAR). Unfortunately, primary care referrals to quitlines are low and the majority of smokers referred fail to call for assistance. This study evaluated a new approach -Ask Advise Connect (AAC) - designed to address barriers to linking smokers with treatment.
A pair-matched-two-treatment arm group-randomized design in 10 family practice clinics in the Houston, TX metropolitan area was utilized. Five clinics were randomized to AAC (intervention) and five were randomized to AAR (control). In both conditions, clinic staff were trained to assess and record the smoking status of all patients at all visits in the electronic health record (EHR), and smokers were given brief advice to quit. In AAC, the names and phone numbers of smokers who agreed to be connected were sent electronically to the Quitline daily, and patients were proactively called by the Quitline within 48 hours. In AAR, smokers were offered a Quitline referral card and encouraged to call on their own. All data were collected between February and December 2011. The primary outcome – impact – was based on the RE-AIM conceptual framework. Impact was defined as the proportion of all identified smokers that enrolled in treatment.
In AAC, 7.8% of all identified smokers enrolled in treatment versus 0.6% in AAR (t(4)=9.19, p=0.0008, OR=11.60 (95% CI 5.53-24.32), a 13-fold increase in the proportion of smokers enrolling in treatment in AAC compared to AAR.
The system changes implemented in AAC could be adopted broadly by other healthcare systems and AAC has tremendous potential to reduce tobacco-related morbidity and mortality.
We propose a two-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma.
First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N=281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N=74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are re-estimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis.
Using the best fitting segregation models in model-based multipoint linkage analysis, we identified two separate peaks on chromosome 17; the first agreed with a region identified by Shete et al. who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD).
Our approach has the advantage of not requiring markers to be in linkage equilibrium unless the minor allele frequency is small (markers which tend to be uninformative for linkage), and of using more of the available information for LOD-based linkage analysis.
Glioma; model-based linkage; segregation; age of onset; prevalence constraint
A genome-wide association (GWA) study is usually designed as a case-control study, where the presence and absence of the primary disease defines the cases and controls, respectively. Using the existing data from GWA studies, investigators are also trying to identify the association between genetic variants and secondary phenotypes, which are defined as traits associated with the primary disease. However, recent studies have shown that bias arises in the estimation of marker-secondary phenotype association using originally collected data. We recently proposed a bias correction approach to accurately estimate the odds ratio (OR) for marker-secondary phenotype association. In this communication, we further investigated whether our bias correction approach is robust for a scenario involving the interactive effect of the secondary phenotype and genetic variants on the primary disease. We found that in such a scenario, our bias correction approach also provides an accurate estimation of OR for marker-secondary phenotype association. We investigated accuracy of our approach using simulation studies and showed that the approach better controlled for type I errors than the existing approaches. We also applied our bias correction approach to the real data analysis of association between an N-acetyltransferase gene, NAT2, and smoking on the basis of colorectal cancer data.
odds ratio; bias; secondary phenotype; SNP; genome-wide association study; frequency-matched study design